Aspects of the disclosure relate to computer vision.
Many existing computer vision algorithms are employed in face detection and other types of imaged based tasks (e.g., the Viola-Jones algorithm). However, many of these algorithms can be resource intensive in terms of processing power, memory usage, and data transfer bandwidth, by manipulating large amounts of image data in order to perform the desired computer vision algorithm in accordance with processor instructions.
Additionally, many existing computer vision algorithms make use of features for classification of objects within an image. Such computer vision algorithms may be used, for example, in face detection and other types of imaged based tasks. Examples of such feature-based algorithms include local binary patterns (LBP) and Haar-like features. However, feature-based algorithms often need to be performed many times (e.g., thousands of times) using different locations, sizes, scales, resolutions, rotations, and/or other parameters of data related to the image. The process can be take a long time and be quite resource intensive in terms of processing power, memory requirements, data transfer bandwidth, etc.
Thus, a need exists for computer vision computation techniques that are more resource efficient and that allow for efficient access to image data.